Predictors and Prospective Course of PANS: A Pilot Study Using Electronic Platforms for Data Collection

Area of Science:

• Pediatric Neurology
• Neuropsychiatry
• Digital Health

Background:

• Longitudinal data on Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS) are scarce, hindering understanding of its course.
• Existing research on PANS is predominantly cross-sectional, limiting insights into symptom evolution.
• Prospective monitoring is crucial for identifying predictors of PANS symptom trajectories.

Purpose of the Study:

• To prospectively monitor neuropsychiatric symptoms in children with PANS using digital platforms.
• To identify baseline clinical characteristics that predict the 12-week course of PANS symptoms.
• To compare compliance and agreement between different electronic data acquisition platforms and parent-child symptom ratings.

Main Methods:

• 20 children with PANS and their parents participated for 12 weeks.
• Weekly symptom ratings were collected via REDCap (Research Electronic Data Capture).
• Parents also completed tri-weekly ratings on the My PANDAS Chart website, with regression analyses applied to longitudinal data.

Main Results:

• A longer delay between PANS onset and enrollment correlated with worsening parent-rated symptoms (p=0.05).
• Higher baseline caregiver burden predicted more severe child-reported symptoms over 12 weeks (p=0.01).
• Weekly REDCap compliance was 86.3%, while tri-weekly My PANDAS Chart compliance was 53.8%; moderate parent-child agreement on symptoms (r=0.55, p<0.0001) was observed.

Conclusions:

• Electronic methods are effective for tracking longitudinal PANS symptoms in children.
• Delayed PANS identification and increased caregiver burden may indicate a more severe short-term symptom course.
• Baseline characteristics can inform predictions about individual PANS trajectories.